This report describes the MUDOS-NG summarization system, which applies a set of language-independent and generic methods for generating extractive summaries. The proposed methods are mostly combinations of simple operators on a generic character n-gram graph representation of texts. This work defines the set of used operators upon n-gram graphs and proposes using these operators within the mult...

While advances in computing resources have made processing enormous amounts of data possible, human ability to identify patterns in such data has not scaled accordingly. Thus, efficient computational methods for condensing and simplifying data are becoming vital for extracting actionable insights. In particular, while data summarization techniques have been studied extensively, only recently ha...

This paper presents our new, querybased multi-document summarization system used in DUC 2007. Current graph-based approaches to text summarization, such as TextRank and LexRank, assume a static graphmodel which does not model how input text emerges. A suitable evolutionary graph model that is related to human writing/reading process may impart a better understanding of the text and improve the ...

Multi-document summarization provides users with a short text that summarizes the information in a set of related documents. This paper introduces affinitypreserving random walk to the summarization task, which preserves the affinity relations of sentences by an absorbing random walk model. Meanwhile, we put forward adjustable affinity-preserving random walk to enforce the diversity constraint ...

We describe a method for extractive summarization of legal judgments using our own graph-based summarization algorithm. In contrast to the connected and undirected graphs of previous work, we construct directed and disconnected graphs (a set of connected graphs) for each document, where each connected graph indicates a cluster that shares one topic in a document. Our method automatically choose...

Multidocument summarization is an automatic process to generate summary extract from multiple documents written about the same topic. Of the many summarization systems developed for English language, the graph based system is found to be more effective. This paper mainly focuses on a multidocument summarizing system for Malayalam Language which follows a graph based approach. The proposed model...

This paper describes a news summarization system using the Fuzzy Graph based Document Model. News articles are modelled as fuzzy graphs whose nodes are sentences and edges are weighted by the fuzzy similarity measure between the sentences. The similarity between sentences is in between 0 and 1. Centrality of the graph retrieves important sentences. The proposed system produces summaries by Eige...

We propose a graph-based ranking method for query-biased summarization in a three-layer graph model consisting of document, sentence and word-layers. The model has a representation that fuses three kinds of heterogeneous information: part-whole relationships between different linguistic units, similarity using the overlap of the Basic Elements (BEs) in the statements, and semantic similarity be...

The increasing amount of biomedical information that is available for researchers and clinicians makes it harder to quickly find the right information. Automatic summarization of multiple texts can provide summaries specific to the user’s information needs. In this paper we look into the use named-entity recognition for graph-based summarization. We extend the LexRank algorithm with information...

Annotation graphs, made available through the Linked Data initiative and Semantic Web, have significant scientific value. However, their increasing complexity makes it difficult to fully exploit this value. Graph summaries, which group similar entities and relations for a more abstract view on the data, can help alleviate this problem, but new methods for graph summarization are needed that han...